Documentation Index
Fetch the complete documentation index at: https://handbook.fiddler.ai/llms.txt
Use this file to discover all available pages before exploring further.
Fiddler LLM Monitoring Introduction
What is LLM Monitoring?
Large Language Models (LLMs) are powerful but introduce unique challenges around accuracy, safety, and reliability. Effective monitoring is critical for detecting issues like hallucinations, toxic content, and performance degradation in production LLM applications.How Fiddler LLM Monitoring Works
Fiddler’s LLM monitoring solution tracks your AI application’s inputs and outputs, then enriches this data with specialized metrics that measure quality, safety, and performance. These enrichments provide visibility into how your LLM applications behave in production, enabling you to:- Detect problematic responses before they impact users
- Identify patterns of failure across your applications
- Track performance trends over time
- Analyze root causes when issues occur
Key Capabilities
- Comprehensive Metrics: Monitor hallucinations, toxicity, relevance, latency, and many other LLM-specific metrics
- Real-time Analysis: Track performance as it happens with intuitive dashboards
- Advanced Enrichments: Generate embeddings, similarity scores, and specialized trust metrics automatically
- Drift Detection: Identify when prompts or responses drift from expected patterns
- RAG-specific Monitoring: For retrieval-augmented applications, analyze retrieval quality and source relevance
Getting Started
Implementing Fiddler LLM monitoring requires just three steps:- Onboard your LLM application to Fiddler by defining its inputs, outputs, and which enrichment metrics you need
- Publish your application data to Fiddler, including prompts, responses, and context
- Monitor performance through dashboards and alerts that track the metrics most important to your use case